The total number of interest points extracted per video is set to equal the number of frames in the video.

even for a small space-time volume, the resulting dimensionality is still quite large: a 10-by-10 pixel box across 5 video frames yields a 500-dimensional feature vector for each of the three gradients.


For both WSJ and Brown only parse annotations though no entity annotations were available.

we could quantify the overall efficiency score E of a MTAL protocol P by where u j denotes the individual annotation task's number of units being annotated (e.g., constituents for parsing) and the task-specific weights are defined by α j .


The rank combination protocol is more directly based on the idea to combine single-task AL selection decisions.

they require large amounts of labeled training data for every level of linguistic processing (e.g., POS tags, parse trees, or named entities).


Those approaches focus on the combination of classifiers in order to improve the classification error rate for one specific classification task.

considering all annotation tasks in the selection process (even if the selection protocol is as simple as the alternating selection protocol) is better than selecting only with respect to one task.


Table 4 compares the heuristic method described above against sampled conditional probability distributions of labels given features 13 .

even though CRR07 uses more constraints and requires additional development data for estimating mixture weights, Ge still outperforms CRR07 when that system is run without applying constraints during inference.


Tagging data to discover the majority label could potentially require a large number of tagged instances before the majority label was definitively identified.

intriguing is the flexibility our scenario provides for the selection of "features of interest" to be driven by error analysis.


Formally: where Z l = (c,l)∈Rt e −αd(l,l ) is a normalization constant, and α is a hyperparameter controlling the possibility of distant migrations (set to 3 in our experiments).

7 In general, identifiability error is incurred when the set of maximizers of E log p θ (x) is non-identifiable.


to simplify notation, we write R t = R(y (i,t) ).


However, Kominek and Black (2006) show that in languages with a less transparent relationship between spelling and pronunciation, such as English, Dutch, or German, the number of letter-to-sound rules grows almost linearly with the lexicon size.

note that we consider not only letter unigrams but all n-grams that fit within the window, which enables the model to assign phoneme preferences to contexts containing specific sequences, such as ing and tion.


A piece of finegrained evidence is formally defined as a quadruple, <topic, person, relation, document>, which denotes the fact that a topic and a person, with a certain relation between them, are found in a specific document.

different types of relations indicate different strength of the connection between expert candidates and queried topics.


With frequency, each expert candidate actually is assigned a prior probability.

we utilize more specific evidences existing in the documents.


Mixture of Contexts: The principal motivation for combining different types of contexts is that different sources may provide complementary evidence.

figure 3 shows the MRR achieved with each context.


Another notable thing about our results is that they seem to be good enough for practical applications.

(Diehl et al., 2006) using header-based traffic analysis techniques.


There are many conceivable ways of combining the two parsers, including more or less complex ensemble systems and voting schemes, which only perform the integration at parsing time.

the experimental results presented so far show that feature-based integration is a viable approach for improving the accuracy of both graph-based and transition-based models for dependency parsing, but they say very little about how the integration benefits the two models and what aspects of the parsing process are improved as a result.


The combined parsing model is essentially an instance of the graph-based model, where arc scores are derived from the output of the different component parsers.

if a parser can improve its accuracy on one class of dependencies, e.g., longer ones, then we can expect to see improvements on all types of dependencies -as we do.


The transition-based model, MaltParser, learns a scoring function s(c, t) ∈ R over configurations and transitions.

given that we are dealing with data-driven models, it should be possible to integrate at learning time, so that the two complementary models can learn from one another.


We used a relatively simple grammar with few additional annotations.

it is standard in the parsing literature to transform grammars into a restricted class of CFGs so as to permit efficient parsing.


For example, while Eisner presented his algorithm as a dynamic programming algorithm which combines spans into larger spans, Yamada and Matsumoto's works by sequentially executing parsing actions that move a focus point in the input one position to the left or right, (possibly) creating a dependency link.

• Item contraction: The inverse of item refinement.


While further development of the Wambaya grammar might make use of some of the remaining 41% of the types, this work suggests that there is a substantial amount of information in the Matrix core type hierarchy which would better be stored as part of the typological libraries.

the remainder of this paper is structured as follows: §2 provides background on the Grammar Matrix and Wambaya, and situates the project with respect to related work.


We see a similar improvement on the EPPS corpus.

the competing alignment models are GIZA Model 4, our implementation of the baseline HMM alignment and our agreement HMM.


For example, the English morpheme "du-" corresponds to the Chinese character (two).

at first glance, word alignment appears to be a more difficult problem than the extraction of parenthetical translations.


For the systems using word lattices, the training data contained the versions of the corpus appropriate for the segmentation schemes used in the input.


We propose a new technique to estimate parameters in paraphrasing.

our contribution is that we combine multiple resources in the framework of SMT for paraphrase generation, in which the dic-tionary definitions and similar user queries are first used as phrasal paraphrases.


The main difference between their methods and ours is that they only used bilingual parallel corpora as paraphrase resource, while we exploit and combine multiple resources.

nearly 60% of the unacceptable paraphrases have semantic errors.


Section 6 shows the experiments and results.

it is useful for paraphrasing some special phrases, such as digital products, computer software, etc, since these phrases often appear in user queries.


This annotated corpus served as both training and test datasets (using cross-validation).

this does not indicate that the comma signifies no relations, only that it does not signify a relation of interest in this work (future work will address relations currently subsumed by this category).


We normalize date and time expressions, and represent numbers as ranges.

the RtE datasets are balanced between entailments and non-entailments, and even in these datasets targeting inference, there are few contradictions.


Globally, we see that contradictions in category (2) occur frequently and dominate the RTE development set.

the scoring measure uses node similarity (irrespective of polarity) and structural information based on the dependency graphs.


We demonstrate that a system for contradiction needs to make more fine-grained distinctions than the common systems for entailment.

our contradiction detection system was developed on all datasets listed in the first part of table 5.


This definition captures intuitions of incompatiblity, and perfectly fits applications that seek to highlight discrepancies in descriptions of the same event.

we simulated the LCC negation corpus, adding negative markers to the RTE2 test data (Neg test), and to a development set (Neg dev) constructed by randomly sampling 50 pairs of entailments and 50 pairs of non-entailments from the RTE2 development set.


One significant issue in contradiction detection is lack of feature generalization.

detecting category (1) contradictions is feasible with current systems.


The number of configurations maintained at any point is a changeable parameter k. In Figure 1 the thermometer beneath the German preposition "in" graphically indicates a high surprisal prediction derived from the dependency parser.

the fact that any member of this parser family derives a useful predictor shows that at least some syntactic properties are reflected in readers' eye fixation durations.


A similarity graph is produced where each node represents a sentence in the collection and the edges between nodes measure the cosine similarity between the respective pair of sentences.

the major limitation of the TF*IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information thus cannot distinguish between "The hero killed the villain" and "The villain killed the hero".


Consider the following sentences: (3) Bob has probably already finished his homework.

uncertainty is not a subtype of subjectivity.


The resulting data set consisted of 46,577 instances of 217 verbs.

some method of capturing the semantic knowledge about the verb's arguments is crucial to the success of a VSD system.


As with most languages, it is relatively simple to assign word class (noun, verb, etc.)

in this work we consider icelandic, a language for which a linguistic rule-based method is the current state of the art, indicating the difficulty this language poses to learning systems.


The log likelihood of a document is the sum of the log likelihoods of all pairs of mentions: (2) where m is the set of mentions in the document, and x is the set of variables representing each pairwise coreference decision x i,j .

for comparison, we also give the results of the COREf-ILP system of Denis and Baldridge (2007), which was also based on a naïve pairwise classifier.


We label this scheme QBUOMM (OMM = "One Minus Max").

when comparing the relative performance of AL algorithms, learning curves can be challenging to in-terpret.


Data We categorized 600 Blog articles from Yahoo!

h&K adopt the χ 2 statistics to calculate an A k score and use web pages as a corpus.


Mixture Model POMDPs combine the efficiency of MDP spoken dialogue systems with the ability of POMDP models to make use of multiple ASR hypotheses.

we call these distributions "MDP belief states".


These systems represent the uncertainty about the dialogue history using a probability distribution over dialogue states, known as the POMDP's belief state, and they use approximate POMDP inference procedures to make dialogue management decisions.

efficiency is maintained because such arbitrary disjunction is not needed to encode the most common forms of uncertainty, and thus the number of MDP states in the set can be kept small without losing accuracy.


The probability p i t from a pruned state r i t is redistributed to unpruned states which are less informative than r i t in their core features.

allowing multiple MDP states provides the representational mechanism necessary to incorporate multiple speech recognition hypotheses into the belief state representation.


On average about 11 CPUs were running concurrently.

we modified GIZA++ to run in parallel, which enabled us to include especially longer sentences into translation model training.


A solution to the problem of homograph (words with multiple distinct meanings) identification is proposed and evaluated in this paper.

in the supervised setup we use the training data described in Section 3.1 for parameter estimation and then use thus fitted models to classify the tuning and test dataset.


The E-step was modified to include only those unlabeled data-points for which the posterior probability was above certain threshold.

each of the above features provides some amount of discriminative power to the algorithm.


Computer programs that can automatically provide similar characters are thus potentially helpful for designing related experiments.

information about radicals as they are defined by the lexicographers is not sufficient.


We now describe our initial attempt at personalizing the ASP framework described above to each asker: • ASP Pers+Text: We first consider the naive personalization approach where we train a separate classifier for each user.

• ASP Group: A more robust approach is to train a classifier on the questions from the group of users similar to each other.


Interestingly, there is little to no data showing that this actually works.

for example, at 37,000 sentences the BioBook corpus is only able to achieve and an f-measure of 82.8% while the Medline corpus is at 83.4%.


In contrast, the out-of-vocabulary rate of biomedical abstracts given the same lexicon is significantly higher at about 25% (Lease and Charniak, 2005).

as is well known, biomedical data is hard on parsers because it is so far from more "standard" English.


This problem can be mitigated by using the rewrite rule shown below, which turns an EDITED-X constituent into the leftmost child of a tree of type X, as long as the original flat tree had X following an EDITED-X constituent and possibly some editing term (ET) categories.

this rule states that the constituent started in the reparandum and repair are ultimately of syntactic types that could be grammatically joined by a conjunction.


This work was partly supported by the FP6 IST LUNA project (contract No.

we also provide: (i) a new kernel to process PASs based on the partial tree kernel algorithm (PAS-PTK), which is highly more efficient and more accurate than the SSTK and (ii) a new kernel called Part of Speech sequence kernel (POSSK), which proves very accurate to represent shallow syntactic information in the learning algorithm.


These weights are given to a weight evaluation function; this function determines how effective a particular set of weights is at a given disambiguation task by calculating an overall score for the weight set: the number of words in the tuning set that were correctly disambiguated.

(3) Tuning to the task that is evaluated improves over tuning to other tasks.


MorphAll is the hardest of the three morphological tagging tasks, subsuming MorphPart and MorphPOS, and DiacFull is the hardest lexical task, subsuming DiacPart, which in turn subsumes LexChoice.

unigramDiac and unigramLex are unigram models of the surface diacritized form and the lexeme respectively, and contain lexical information.


The EM models had higher normalized log-likelihood than the Automatic model in 50 out of 50 runs.

users are typically unwilling to endure this kind of experimentation.


The morpheme feature M F (m i ) is a pair of the surface string and the part-of-speech of m i .

it is difficult to obtain an enough corpus in the real world, because there are increasing the number of NEs like personal names and company names.


We trained the kernel version of an averaged perceptron model (Freund and Schapire, 1999), using a polynomial kernel with degree 4 and additive term 1.

the method yields a model capable of matching events with an F-measure of 66.5%.


In recent years, Bracketing Transduction Grammar (BTG) proposed by (Wu, 1997) has been widely used in statistical machine translation (SMT).

the linguistic knowledge is automatically learned from source-side parse trees through an annotation algorithm.


This new language model trained on the quasi-corpus is then used together with the language model trained on the general-domain data (original training data) to produce a new list of N-best hypotheses under our self-enhancement framework.

an alternative solution for enhancing the translation model is through self-training (Ueffing, 2006;Ueffing et al., 2007) which re-trains the source-target N-best hypotheses together with the original training data, and thus differs from ours in the way of new phrase pairs extraction.


Moses uses translation weights and lexical weights to measure the quality of a phrase translation pair.

to run the decoder, we set ttable-limit=20, distortion-limit=6, stack=100.


when α=0.3, the system obtains the highest BLEU score of 25.31, which achieves an absolute improvement of 0.87 over the baseline.

(2006) used paraphrases of the trainig corpus for translating unseen phrases.


Retain for further consideration the 4 state split of s, the best merge back to 3 states among the 6 ways, the best merge back to 2 states among the 7 ways, and the merge back to 1 state.


This finding could have important implications for the development of spoken dialog systems (SDS).

interruptions (I) and long inter-turn latency (L)-long simultaneous pauses by the speakers-are generally perceived as a sign of poorly coordinated dialogues.


It is also worthwhile noting that in such populations, out of 100 best evolved measures, all but four of them featured a condition identical to that of M 13 (POS (b) = X).

for example, collocations kosilica za travu (lawn mower) and digitalna obrada podataka (digital data processing) were ranked at the 22th and 34th percentile according to Dice, whereas they were ranked at the 97th and 87th percentile according to M 13 .


The statistical information about an n-gram can be the frequency of any part of the n-gram.

the evolved association measure may be a complex expression that defies interpretation, in which case it may be treated as a blackbox suitable for the specific task of collocation extraction.


Such analyses can eventually be helpful for applications like question answering, for instance, and will be investigated in the future.

we employed the data provided by the PropBank and VerbNet projects to extract lexical semantic information.


One possibility, which we explore in this paper, is that it is the generic nature of the fusion which causes problems.

marsi and Krahmer (2005) propose an algorithm which is capable of producing both fusion types.


The emergence of comparative evaluation in NLG raises the broader question of how systems that generate language should be compared.

the correlation figures between the taskperformance measures and all others are weak and not significant.


The Kappa statistics for those 6 different annotators varies from 0.11 to 0.35 for different meetings.

this may reduce some accidental noise in human evaluation.


The correlation results are presented in Table 1.

(Zhu and Penn, 2005) found a statistically significant difference between the ASR-inspired metrics and those taken from text summarization (e.g., RU, ROUGE) on a subset of the Switchboard data.


(2006) report that they gave the Topic description frequency a much higher weight than the Content word frequency.

processing time of more than several seconds per summary should be considered unacceptable, in particular, if you bear in mind that using such a system should help a user to process lots of data faster.


Formally, we assign each attachment a numerical depth (0 for top, 1 for the position below the top, and so on) and then we find argmax a 1 ,...,an n 1 P (a 1 ) .

lex Pairs the headword of the modifier with the noun it is modifying.


By construction, a 2 r v symbol is the right successor of a non-terminal R p u .

a second-order dependency structure is illustrated in Figure 1 that involves two adjacent dependents, Miles and with, of a single head, hit.


The parse trees used in our system are lexicalized binary trees.

additional feature categories are included separately.


Foster generates an ungrammatical version of the WSJ treebank and uses this to train two statistical parsers.

while statistical, data-driven parsing has solved the robustness problem, it is not clear that it is has solved the accurate robustness problem.


The researches on system's intention prediction have been treated as a part of researches on dialogue models such as a finite-state model, a frame-based model (Goddeau, 1996), and a plan-based model (Litman, 1987).

speaker's intention prediction modules can be widely used as a pre-processor for reducing the search space of an automatic speech recognizer.


Common intuition equates large margins with high prediction confidence.

we present active learning with confidence using a recently introduced online learning algorithm called Confidence-weighted linear classification.


Every learning sample is represented by an n = |F | dimensional binary vector x. x i = 1 iff the feature f i is active in the given sample, 0 otherwise.

support Vector Machines (sVMs) have gained popularity as they constantly outperform other learning algorithms for many NLP tasks.


In addition to specifying the headwords of inter-propositional facets (stick and is, in 1e), we also note up to two key facets from each of the propositions that the relation is connecting (b, c, and d in example 1).

the word pattern is considered the governor of pigments; whereas, conversely the word leaves is treated as the governor of bunch because it carries more semantics.


In fact, WordNet, with its broad coverage and easy accessibility, has become the resource of choice for WSD.

these results suggest that closely related word senses may be represented in the mental lexicon much like a single sense, perhaps sharing a core semantic representation.


the sizes of the final training sets vary between 2,000 and 3,600 words depending on the language.


Interestingly, apart from a few exceptions, the results are rather good for all the pairs of training and test language.

finally, words that are not really grammatical compounds, but due to the user forgetting to input the blankspace between the words (like desktopcomputer) are split.


Finally, words that are not really grammatical compounds, but due to the user forgetting to input the blankspace between the words (like desktopcomputer) are split.

being able to identify that achzigerjahre should be decompounded as achzig+jahre (where achzig is a misspelled variation of achtzig) is still useful in obtaining some meaning from the user query and in helping the spelling correction system.


Dealing with such an unbalanced performance, we definitely need to put enough high weight on the training data from Electronics.

a well-known method called meta-learning (ML) has been shown to be very effective (Vilalta and Drissi, 2002).


The research work described in this paper has been partially supported by the Natural Science

it can still increase the average accuracy from 0.8325 to 0.8625, an impressive relative error reduction of 17.91% over baseline.


Previous research has focused on the percentage of out-of-vocabulary (OOV) terms to explain changes in keystroke savings Wandmacher and Antoine, 2006).

we attempt to evaluate word prediction as realistically as possible.


MapReduce is an attractive framework because it shields the programmer from distributed processing issues such as synchronization, data exchange, and load balancing.

the complexity of our pairwise similarity algorithm is tied to the number of document pairs that are emitted by the mapper, which equals the total number of products required in O(N 2 ) inner products, where N is the collection size.


On a collection consisting of approximately 900,000 newswire articles, our algorithm exhibits linear growth in running time and space in terms of the number of documents.

the impact of this technique on effectiveness (e.g., in a query-by-example experiment) has not yet been characterized.


Finally, we will explore the theoretical foundations and limitations of heterogeneous transfer learning as well.

table 2 shows the performance with respect to all comparison methods on each of the image clustering tasks measured by the entropy criterion.


Then, we encoded the training text as well as the translated target text together, in terms of the information theory.

in Figure 3, the latent variables Z depend not only on the correlation between image instances V and image features F, but also the correlation between text features W and image features F. the auxiliary socially-annotated image data can be used to help the target image clustering performance by estimating good set of latent variables Z.


These semantic roles appear in many frames, because they have general meanings that can be shared by different frames.

groups of role descriptors and semantic types have large weight values for the first word and supersense features, which capture the characteristics of adjunctive phrases.


The experimental result of the role classification task showed a 19.16% of the error reduction and a 7.42% improvement in the macroaveraged F1 score.

suppose we have a model P (y|f, x) which yields the conditional probability of the semantic role y for given f and x.


This is a necessary condition for correctness of the full constituent, but not a sufficient one.

the automatic parser, however, attaches the relative clause low -to asbestos (figure 9).


For example, the verb open encodes the OPENER as Arg0, the OPENEE as Arg1, and the beneficiary of the OPENING action as Arg3.

because most SRL systems are designed to label constituents rather than headwords, it is necessary to project the roles up the derivation to the correct constituent in order to make a meaningful comparison of the system's performance.


Finally Q-10-method achieved an f-score of 93.6% on WSJ section 2∼18 and 20∼22, better than that of Q-0method and comparable with that of Q-10-method in Section 3.1.


We propose a method to enhance dependency parsing in one language by using a translated treebank from another language.

section 4 describes a dependency parser for Chinese as a baseline.


Although supervised learning methods bring stateof-the-art outcome for dependency parser inferring (McDonald et al., 2005;, a large enough data set is often required for specific parsing accuracy according to this type of methods.

as fewer language properties are concerned, our approach holds the more possibility to be extended to other language pairs than theirs.


The grammar trainer attempts six iterations of splitting, merging, and smoothing before returning the final grammar.

• MF (Mittelfeld or 'middle field') is an optional field bounded on the left by LK and on the right by the verbal complex VC or by NF.


In the monolingual setting, there is a wellknown tree sampling algorithm (Johnson et al., 2007).

we also reimplemented the original EM version of CCM and found virtually no difference in performance when using EM or Gibbs sampling.


For instance, we are not interacting with a human user, so the cost of interaction is lower.

reinforcement Learning for Language Processing reinforcement learning has been previously applied to the problem of dialogue management (Scheffler and Young, 2002;roy et al., 2000;Litman et al., 2000;Singh et al., 1999).


A reasonable heuristic which uses weighted number-and string-matching achieves 26.7.

we did not tune any hyperparameters, but rather used generic values which worked well enough across all three domains.


When we repeat this process, it is expected to mix rapidly because it implicitly considers all possible segmentations of the given string at the same time.

in order to extract "words" from text streams, unsupervised word segmentation is an important research area because the criteria for creating supervised training data could be arbitrary, and will be suboptimal for applications that rely on segmentations.


Here, "$" is a special token representing a sentence boundary in language model-ing (Brown et al., 1992).

for example, we are pleased to say that we can now analyze (and build a language model on) "The Tale of Genji", the core of Japanese classics written 1,000 years ago ( figure 6).


Note that NPYLM maximizes the probability of strings, equivalently, minimizes the perplexity per character.

japanese word segmentation, with all supervised segmentations removed in advance.


In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference.

we remove the corresponding customers from the character HPYLM using the inverse procedure of adding a customer in Section 2.


We can then check numerically how well our estimator performs with respect to the "true" value.

in this paper, we have proposed a new nonparametric estimator of vocabulary size that takes into account the LNRE property of word frequency distributions and have shown that it is statistically consistent.


For ω to be an unlikely word, we would like this probability for large n to remain strictly between 0 and 1.

quantities such as vocabulary size change with sample size.


The general tree-growing algorithm has no notion of the letter distance, but instead chooses the letters on the ba-sis of their estimated information gain (Manning and Schütze, 1999).

the example in the pool that maximizes the disagreement among the predictions of the committee members is selected.


Hence, QBB selected 10 words from the 2000 candidates.

only a single Italian dataset was used, and the results do not necessarily generalize to other languages.


We have manually verified and corrected around 240,000 pairs to clean up the corpus.

a number of transliteration studies have touched on the alignment issue as a part of the transliteration modeling process, where alignment is needed at levels of graphemes and phonemes.


The new algorithm worked very well with the Polish Morfologik dataset and compares well with the Stempel algorithm (Białecki 2008).

the results showed that lemmatizer was as good as the stemmer for Swedish, slightly better for Danish and Norwegian but worse for Greek.


The guidelines for the construction of the training material are not always known to us.

there is no preferential order between the children of a rule and all paths away from the root must be regarded as equally valid.


This means that the new algorithm is good at generalizing over small groups of words with exceptional morphology.

the training algorithm generates a data structure consisting of rules that a lemmatizer must traverse to arrive at a rule that is elected to fire.


When tested with inflected out-ofvocabulary (OOV) words Stempel produces 95.4 percent correct stems, of which about 81 percent also happen to be correct lemmas.

the more complex rules involving infix or prefix subpatterns or combinations thereof are less well-founded than the simple suffix-only rules.


There have been some attempts in creating stemmers or lemmatizers automatically.

the bulk of 'normal' training words must be bigger for the new affix based lemmatizer than for the suffix lemmatizer.


Many solutions have been proposed to address the above two issues.

we present our solutions that are able to extract translation rules and decode translation results for our model very efficiently.


Possible improvements includes: (a) using advanced semantic and parsing technologies to detect the support and relevant sentences for infobox and outline; (b) summarizing multiple articles in a wiki category; and (c) exploring the mapping from close-class terms to open-class terms for more links between passages is likely to forward some interesting results.

these heuristic rules include: (i) edit distance between linked wiki concept and candidates in normal text is larger than a predefined threshold; and (ii) partially overlapping words beginning with capital letter, etc.


from a wiki article 'Carolyn Bessette Kennedy' 2 , we can find 11 distinctive terms, such as after, graduate, Boston, University,1988, go, work, Calvin, Klein, store, Boston, if stop words are ignored.

to recover the hidden associations between sentences, Ye et al.


eijr, wj): As described in Section 3.2.1: Calculates scorej(e ijl ) for all excerpts for document di and topic tj, using parameters wj.

family members of affected individuals should also receive regular clinical evaluations to detect any symptoms and physical characteristics that may be potentially associated with Three M syndrome or heterozygosity for the disorder.


Using this template to create the biography of a new actor will ensure that its information coverage is consistent with existing human-authored documents.

• Automatic template creation: Templates are automatically induced from humanauthored documents.


We use ROUGE, an evaluation metric employed at the Document Understanding Conferences (DUC), which assumes that proximity to human-authored text is an indicator of summary quality.

prenatal testing may be available for families in which the disease-causing mutations have been identified in an affected family member in a research or clinical laboratory.


The random system uses even less information in generating a story (entityaction relationships are chosen at random without taking note of the MI scores).

given the sentence the dog chases the duck as input, our generator assumes that either dog or duck will be the subject of the following sentence.


Regressing the best predictors from Table 2 against the interest ratings yields a correlation coefficient of 0.608 (p < 0.05).

a story created simply by concatenating sentences in isolation will often be incoherent.


The F-measure improves, on average, by 25 percentage points over the OpTopic system, and by 17 percentage points over the OpPMI system.

we apply the method described in Section 3.1 to the downloaded web pages.


For example, both the iPhone and Blackberry have keyboards, but we observed in our corpus that positive opinions about the keyboard are associated with the pro Blackberry stance.

we implemented two baselines: the OpTopic system that uses topic information only, and the OpPMI system that uses topic as well as related word (noun) information.


The opinions occurring in the conceded part are interpreted in reverse.

several researchers have worked on similar tasks.


The co-training algorithm In the training phase, the co-training algorithm learns two separate classifiers: C en and C cn .

in the above experiments, all features (unigram + bigram) are used.


By the same reasoning, we will find more frequent labels than the underlying real distribution of a complete annotation.

in this paper, we have proposed a taskindependent, general method to analyse annotation schemes.


The semantic role inventory, thus, should show a certain correlation with the inventory of grammatical functions.

notice however that both sampling methods used to collect the counts are likely to slightly overestimate frequent labels.


(2006) have identified a number of problems with BLEU and related n-gram-based scores: (1) BLEUlike metrics are unreliable at the level of individual sentences due to data sparsity; (2) BLEU metrics can be "gamed" by permuting word order; (3) for some corpora and languages, the correlation to human ratings is very low even at the system level; (4) scores are biased towards statistical MT; (5) the quality gap between MT and human translations is not reflected in equally large BLEU differences.

together with the subject mismatch, MtR correctly predicts a low score (rank 5/5).


Each language consists of 1500-2800 sentence pairs produced by 7-15 MT systems.

incorporation of syntactic knowledge has been the focus of another line of research.


We then apply this approach to find out about the advantages and challenges of including linguistic features in meta-evaluation criteria.

in Section 4 the correlation between human assessors and metrics is computed, with a discussion of its drawbacks.


Figure 4: Counter sample ratio for high vs low metric scored translations In order to shed some light on the reasons for the automatic evaluation failures when assigning low scores, we have manually analyzed cases in which a metric score is low but the quality according to humans is high (Q Nm ≤ 3 and Q h ≥ 7).

the main goal of our work is to analyze to what extent deep linguistic features can contribute to the automatic evaluation of translation quality.


This is due to the fact that a high word overlapping with human references is a reliable evidence of quality.

then they reward precision at the cost of recall in the evaluation process, and this explains the high correlation with human judgements at system level with respect to segment level.


In the case of Metric B, the high scored translations can achieve both low or high quality according to human judges but low scores ensure low quality.

this approach has proven very effective in recent evaluation campaigns.


With respect to correlation at system level, the main problem is that the relative performance of different metrics changes almost randomly between testbeds.

the most commonly used metrics are still based on n-gram matching.


Without considering syntactic constraints from the parse tree, the decoder makes wrong decisions not only on phrase movement but also on the lexical selection for the multi-meaning word " " 1 .

in section 3 we elaborate the syntax-driven bracketing model, including feature generation and the integration of the SDB model into phrase-based SMT.


In the absence of linguistic knowledge, the system models linguistic structure using an SCFG that contains only one type of nonterminal symbol 1 .

although this did not yield signi cant improvements, Marton and Resnik (2008) and Chiang et al.


Rather than appealing to annotationdriven syntactic modeling, we address this problem by observing the in uential role of function words in determining syntactic structure, and introducing soft constraints on function word relationships as part of a standard log-linear hierarchical phrase-based model.

the system's lack of linguistic commitment is also responsible for one of its great-est drawbacks.


A similar idea was applied to word alignment by Lacoste-Julien et al.

a crucial fact about dependency grammars is that words have preferences about the number and arrangement of arguments and modifiers they accept.


In fact, the only significant differences in attachment score here are the positive differences between MST Malt and all other systems for Arabic and Danish, and the negative difference between MST-06 and all other systems for Turkish.

for these languages, there is no statistical difference between S u and MST Malt , which are both significantly better than all the other parsers, except S pp for Czech (Mc-Nemar's test, α = .05).


The system reuses standard techniques for building projective trees by combining adjacent nodes (representing subtrees with adjacent yields), but adds a simple mechanism for swapping the order of nodes on the stack, which gives a system that is sound and complete for the set of all dependency trees over a given label set but behaves exactly like the standard system for the subset of projective trees.

we have presented a novel transition system for dependency parsing that can handle unrestricted non-projective trees.


, n from B to Σ (using SHIFT) and then remove them from Σ (using LEFT-ARC l or RIGHT-ARC l ), which will produce a tree with root 0.

the system of Nivre (2008b) is sound but not complete for the class of all dependency trees.


Klein and Manning (2004) use a carefully initialized and structured generative model (DMV) in conjunction with the EM algorithm to get the first positive results on unsupervised dependency parsing.


The first rule sets main verbs to dominate auxiliary verbs.

the main difference between this work and theirs is the source of the information (a linguistic infor-mant vs. cross-lingual projection).


For the discriminative model this did not affect results significantly but improved them slightly in most cases.

we are seeing improvements not only directly from the constraints we enforce but also indirectly as types of edges that tend to get ruled out.


We simply attach all fragments onto the virtual root node.

for instance, the text style of WSJ is newswire, and most of the sentences are statements.


Childes This is another out-domain test set from the children language component of the TalkBank, containing dialogs between parents and children.

the unbalanced distribution of linguistic phenomena in the training data leads to inadequate parser output structures.


Although MT may be used in solving this task, it is only used by the algorithms -the final evaluation is done in the source language.

all three annotators were native English speakers who were not system developers for any of the 5W systems that were being evaluated (to avoid biased grading, or assigning more blame to the MT system).


Similar to bilingual co-training, classifiers for two languages cooperated in learning with bilingual resources in bilingual bootstrapping.

our research on bilingual co-training sprang from a very simple idea: perhaps training data in a language can be enlarged without much cost if we translate training data in another language and add the translation to the training data in the original language.


In the origi-nal SEAL, the Extractor requires the longest common contexts to bracket at least one instance of every seed per web page.

snow et al (snow et al., 2006) use known hypernym/hyponym pairs to generate training data for a machine-learning system, which then learns many lexico-syntactic patterns.


Every edge from node x to y actually has an inverse relation edge from node y to x that is not shown here (e.g., m 1 is extracted by w 1 ).

when seeds are noisy, such common contexts usually do not exist.


This configuration yields the best results: it maintains WordNet's precision and adds nearly 50% to the recall increase of WordNet over No Expansion, indicating the substantial marginal contribution of Wikipedia.

this substantially reduces the Dice score for those cases in which the LHS co-occurs mainly with the full NP.


We present extraction methods geared to cover the broad range of the lexical reference relation and analyze them extensively.

we further suggested a filtering method which significantly improved performance.


All-N The Be-Comp extraction method yields mostly hypernym relations, which do not exploit the full range of lexical references within the concept definition.

such errors mostly occur when a general term, e.g.


Indeed, the commonly used hyponymy, synonymy and some cases of the meronymy relations are special cases of lexical reference.

the remainder of this section describes our methods for extracting rules from the definition sentence and from additional Wikipedia information.


The primary requirement (and challenge) here is dealing with multi-membership: An item may belong to multiple semantic classes; and we need to discover as many as possible the different semantic classes the item belongs to.

query set and labeling: We have volunteers to try Google Sets 4 , record their queries being used, and select overall 55 queries to form our query set.


Wu's model can be understood as a strict hierarchical maximum-alignment method.

furthermore, using a product of experts (Hinton, 2002), we combine the model with a complementary logistic regression model based on state-of-the-art lexical overlap features.


A morphological analysis of a word w consists of a (possibly structured) POS tag t, together with one or several lemmas, which are the possible basic forms of w when it has tag t. As an example, Table 1 illustrates the morphological analyses of several words taken from the CELEX lexical database of English (Baayen et al., 1995) and the Multext-East lexicon of Bulgarian (Erjavec, 2004).

the model name local FS denotes a joint log-linear model which has only word-internal features.


For the LSA model, we had to use a more drastic cutoff to fit the singular value decomposition computation into memory: we replaced words appearing 10 times or fewer with the * UNKNOWN * symbol.

we investigate empirically the effects of our smoothing techniques on two sequence-labeling tasks, POS tagging and chunking, to answer the following: 1.


In this paper, we first introduce MIRA to joint word segmentation and POS tagging and show very encouraging results.

the goal of our learning algorithm is to learn a mapping from inputs (unsegmented sentences) x ∈ X to outputs (segmented paths) y ∈ Y based on training samples of input-output pairs S = {(x t , y t )} t t=1 .


Figure 7 shows an example of the extra linefeed insertion.

the result R which maximizes the conditional probability P (R|B) is regarded as the most appropriate result of linefeed insertion, and calculated by dynamic programming.


In recent years, there exist a lot of researches about automatic captioning, and the techniques of automatic speech recognition (ASR) aimed for captioning have been developed (Boulianne et al., 2006;Holter et al., 2000;Imai et al., 2006;Munteanu et al., 2007;Saraclar et al., 2002;Xue et al., 2006).

this research was partially supported by the Grant-in-Aid for Scientific Research (B) (No.


Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models.

all of these sample selection approaches use the confidence-based labeling.


• Energy range (4 features): maximum energy, minimum energy, mean energy, and energy range (difference between maximum and minimum energy).

we believe that using the hard decisions from the two classifiers along with the agreement-based rule is not enough to label the unlabeled samples.


For the transcriptbased systems, the sim 1 and sim 2 scores in this paper are measured by the number of words shared between a sentence and a sentence/document set mentioned above, weighted by the idf scores of these words, which is similar to the calculation of sentence centroid values by Radev et al.

this model is of interest due to its independence from spoken language transcription, an error-prone and resource-intensive process, its ability to integrate multiple sources of information on the same topic, and its novel use of acoustic patterns that extends previous work on low-level prosodic feature detection.


After the multiple DTW paths are calculated, the optimal subpath on each is then detected in order to find the local alignments where the similarity is maximal, which is where we expect actual matched phrases to occur.


For example, in Figure 1 killed occurs before all other shared words in one document and after all of these in the other, so a single alignment path that monotonically seeks the lower right-hand corner of the similarity matrix could not possibly match all common words.

for error-prone transcripts of spoken documents, an abstractive method still seems to be too ambitious for the time being.


The first direct use of packed forest is proposed by .

there exists at least one consistent node v ′ on another side satisfying: v ′ is said to be a counterpart of v. We use τ (v) to denote the set of counterparts of v. A frontier node often has multiple counterparts on another side due to the usage of unary rules in parsers.


(2006), we distinguish between minimal and composed rules.

we obtain the following two minimal frontier tree pairs for the source frontier node "PP 4 ": To maintain a reasonable rule table size, we restrict that the number of nodes in a tree of an STSG rule is no greater than n, which we refer to as maximal node count.


Minimizing risk has been shown to improve performance for MT (Kumar and Byrne, 2004), as well as other language processing tasks (Goodman, 1996;Goel and Byrne, 2000;Kumar and Byrne, 2002;Titov and Henderson, 2006;Smith and Smith, 2007).

future work may show additional benefits from fast consensus decoding.


From the results we can see that combination based on co-decoding's outputs performs consistently better than that based on baseline decoders' outputs for all n-best sizes we experimented with.

different from system combination and MBR decoding, which postprocess the n-best lists or word lattice of machine translation decoders, in our method multiple machine translation decoders collaborate by exchanging partial translation results.


To develop the intuition, we first present a brute-force algorithm in Figure 3.

this is necessarily true, but it is interesting to see that most of the improvement is obtained just by moving from a unigram to a bigram model.


To obtain p(y | x) above, we need to marginalize over a nuisance variable, the derivation of y.

1 For example, though machine translation (MT) seeks to output a string, typical MT systems (Koehn et al., 2003;Chiang, 2007) will also recover a particular derivation of that output string, which specifies a tree or segmentation and its alignment to the input string.


Statistical models in machine translation exhibit spurious ambiguity.

13 For n ≤ m, any of these decoders (14)-(16) may be implemented efficiently by using the n-gram variational approximations q * to rescore HG(x)-preserving its hypergraph topology, but modifying the hyperedge weights.


They alleviate the computation cost somehow by using aggressive beam pruning, which might be sensible for their relatively small task (e.g., input sentences of < 10 words).

because q * only approximates p, y * of (13) may be locally appropriate but globally inadequate as a translation of x.


For CLANG, 300 instructions were randomly selected from the log files of the 2003 ROBOCUP Coach Competition and manually translated into English (Kuhlmann et al., 2004).

1 accurate syntactic parsers are available for many languages and could potentially be used to learn more effective semantic analyzers.


Class-Entropy (CE): In all models, the inferred latent annotated concepts can be used to smooth the attribute rankings for each labeled attribute set.

fixed-hierarchy LDA can be extended to perform automatic sense selection by placing a distribution over the leaf concepts c, describing the prior probability of each possible path through the concept tree.


We thank Peter Lang Publisher for allowing us to use the relevant contents of the book (Yang, 2007) in this article.

the condition given by the input specification is t t =t r , while the condition required by the REP aspect is t t <t r .


Lee and Hsu's Chinese to English machine translation system (1990), Li, Wong, and Yuan's temporal information-extraction system (2001), Xue's machine learning system (2008), and Xue, Zhong & Chen's tense annotation system (2008).

except for the mismatch of the temporal relations, the conceptual feature repeatable required by the experiential aspects is also absent in the input specification.


paper, model, toy) more typically assume the part of speech (POS) tag of nouns, unlike our other modifiers (e.g., soft, large, strong) whose typical POS tag is adjective.

the acquisition matrix was 64 x 64 with 3.125 x 3.125 x 5-mm voxels.


Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic representations in humans by making it possible to directly observe brain activity while people comprehend words and sentences.

the multiplicative model predicts that the visual aspects will be emphasized when a participant is thinking about strong dog, while the additive model predicts that, in addition, the behavioral aspects (e.g., eat, smell, and hear) of dog will be emphasized.


There have been recently increasing concerns with the need for anaphora resolution to make NLP applications such as IE and MT more reliable.

in addition, the difference between the static and dynamic cache models demonstrates that the dynamic one is always better then the static.


A typical machine learning-based approach to zero-anaphora resolution searches for an antecedent in the set of candidates appearing in all the preceding contexts.

in this experiment, we take the ellipsis of nominative arguments of predicates as target zeropronouns because they are most frequently omitted in Japanese, for example, 45.5% of the nominative arguments of predicates are omitted in the NAiST Text Corpus (iida et al., 2007b).


One is the Centering-derived model which only stores the preceding 'wa' (topic)-marked or 'ga' (subject)-marked candidate antecedents in the cache.

we also investigate whether correct zero-anaphora resolution contributes to the dynamic cache model or not.


Because of examples like this, "but-but" is a very useful word pair feature indicating Cause, as the but would have been removed for the artifical Contrast examples.

graphBank annotations do not differentiate between implicits and explicits, so it is difficult to verify success for implicit relations.


A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems.

the reason why eigenvectors with large eigenvalues are retained is that they capture the largest variance in the data.


is considered, we extract the following component match features: 22,1994 In our experiments we observed that converted questions have at most one subject, head, object and a few modifiers.

we retrieved around 20 candidate sentences for each of the 202 test questions and manually labeled each q/a pair as true/false entailment to compile 4037 test data.


WikiAnswers is a social Q&A site similar to Yahoo!

the experiments bear strong evidence that several datasets encode different and complementary types of knowledge, which are all useful for retrieval.


This is a case of a weak match with only "Halloween" as matching term.

the quality of the semantic information encoded in the translation tables has never been assessed intrinsically.


The proposed LAICA approach can be regarded as a discriminative one since it not only considers the reference characters but also those wrongly recognized characters.

of course we eliminate some strong competitive characters in this way but we can not guarantee that reference characters will then have high enough PP to be ranked first in its own cluster.


Adding new words into the lexicon, on the other hand, offers explicit reinforcement in PP of the reference characters.

the previously mentioned lexicon adaptation approaches and corresponding language models were used in the same speech recognizer for the spoken document indexing.


Of course the paths going through a sub-edge e i should be definitely more than the paths through the corresponding full-edge e. As a result, P (e i |A) should usually be greater than P (e|A), as implied by the intuition.

the reference characters between two anchors then become our focus segment and their PPs should be enhanced.


Bootstrap initial word segmentation by maximum-matching algorithm (Wong and Chan, 1996) 2.

adding fewer words means that longer words are added.


although we could use our model directly as a decoder to perform translation, its simple hierarchical reordering parameterisation is too weak to be effective in this mode.


Following the broad shift in the field from finite state transducers to grammar transducers (Chiang, 2007), recent approaches to phrase-based alignment have used synchronous grammar formalisms permitting polynomial time inference (Wu, 1997;Cherry and Lin, 2007;Zhang et al., 2008b;Blunsom et al., 2008).

markov chain monte Carlo sampling allows us to perform inference for the model described in 4.1 without restricting the infinite space of possible translation rules.


We hypothesise that this property may be particularly useful for syntactic translation models which often have difficulty with inconsistent word alignments not corresponding to syntactic structure.

we incorporate priors encoding our preference for a briefer and more succinct grammar, namely that: (a) the grammar should be small, with few rules rewriting each non-terminal; and (b) terminal rules which specify phrasal translation correspondence should be small, with few symbols on their right hand side.


We allow two types of rules: non-terminal and terminal expansions.

the efficacy of this model is unclear due to the small-scale experiments and the short sampling runs.


In this paper, all the feature functions used in the log-linear model are n-gram probabilities.

the BLEU measure computes the geometric mean of the precision of n-grams of various lengths between a sentence realization and a (set of) reference(s).


Table 3, for instance, contains the labels BRIDGING-CON-TAINED and PARTITIVE-CONTAINED, going back to Prince's (1981:236) "containing inferrables".

by calculating strong asymmetries between pairs of IS labels, and establishing the most frequent syntactic characteristics of these asymmetries, we designed a new set of features for a log-linear ranking model.


We believe, however, that despite this shortcoming, we can still take advantage of some of the insights gained from looking at the influence of IS on word order.

in the work described here, we use the scheme of Riester (2008b).


D-GIVEN entities are subdivided according to whether they are repetitions of their antecedent, short forms thereof, pronouns or whether they use new linguistic material to add information about an already existing discourse referent (label: EPITHET).

we want to know how much of the improvement gained is due to the IS asymmetries, and how much the syntactic asymmetries on their own can contribute.


Here, We define function I(•), which maps word y i to the index of the word in the original sentence.

the n-gram probability sometimes disagrees with our intuition in terms of sentence compression.


If the applications are not considered, all units of an input sentence that can be paraphrased using the PTs will be extracted as source units.


Let (s i ,t i ) be a pair of paraphrase units, their paraphrase likelihood is computed using a score function φ pm (s i ,t i ).

replacement rate (rr): rr measures the paraphrase degree on the development set, i.e., the percentage of words that are paraphrased.


The bilingual parallel data for SMT are easy to collect.

it has been shown that the coverage of the paraphrase patterns is not high enough, especially when the used paraphrase patterns are long or complicated (Quirk et al., 2004).


More specifically, both baselines recast the cause identification problem as a set of 14 binary classification problems, one for predicting each shaper.

for the four non-minority classes, the benefits obtained from using the bootstrapped documents can also be obtained by simply adjusting the CT.


"), is tokenized to get a set of tokens {'gud', 'plc', '2', 'buy', '10s', 'strng', 'on9'}.

for example, for a given SMS token "byk", idf of "bike" will be more than that of commonly occurring word "back".


the number of trees generated for the same structure increases exponentially with the number of equal tags in the tree.


Preparing labeled data, however, is very expensive.

this, is less of an issue for parsing web search queries, as they are usually very short (2.8 words/query in average (Xue et al., 2004)).


This feature helps decide if those pairs sharing the same pattern are really translations; 3) Length: the length of p i .

if the irregularity of a pattern is low, we can be confident that pairs sharing this pattern have a reliably similar inner relationship with each other.


Note that those bilingual names do not follow the parenthesis pattern.

we then use a linking algorithm which takes both translation and transliteration into consideration to link words across the two collections.


Second, we will report some experimental results on a manually constructed test data set to analyze the impact of each part of our method.

since the number of bilingual web sites is quite small, these methods can not yield a large number of bilingual sentences.


In future studies, we will also gather data on these additional non-verbal behaviours, and we expect to find higher correlations with subjective judgements.

in the system that was used in the current study, the robot instructs the user on building a particular compound object, explaining the necessary assembly steps and retrieving pieces as required, with the user performing the actual assembly actions.


The overall correct-assembly rate was correlated with the overall rate of remembering objects: R 2 = 0.20, p < 0.005.

if the user did not remember, the system explained the building process again; if they did remember, the system simply told them to build another one.


The task success measures addressed user success in the two main tasks undertaken in these interactions: assembling the target objects following the robot's instructions, and learning and remembering to make a snowman and an L shape.

subjects who said that they did remember how to build a snowman or an L shape the second time around were no more likely to do it correctly than those who said that they did not remember.


Recent studies use user simulations to generate training corpora to learn dialog strategies automatically ( (Williams and Young, 2007), (Lemon and Liu, 2007)), or to evaluate dialog system performance (López-Cózar et al., 2003).

each student utterance was manually annotated for certainty (certain(cert), notcertain(ncert)) in a previous study based on both lexical and prosodic information 5 .


Now we discuss how to set up user action probabilities in the KC Model.

when using simulations to test a dialog system, our results suggest that once we have enough human user data, it is better to use the data to train a new simulation to replace the handcrafted simulation.


We consider a simulated action associated with a higher probability to be ranked higher than an action with a lower probability.

the measures introduced in Section 4.1 and Section 4.2 have less differentiating power since they compare at the corpus level.


For the second Manual KC model (M2), we allow differences among the clusters within the same difficulty group.

when building a new dialog system, usually no data or only a small amount of data is available.


This group had an average WD score of .199, better than the rest of the group at .268.

people have a desire to be entertained, but dialogue annotation can often be tedious and difficult.


Utterance 4 has a comparatively large number of votes, but it is not considered a peak because its neighbour, utterance 5, is higher.

participants do not always show acceptance of these items explicitly.


(2008), which contains 864 abbreviation definitions collected from 1,000 MED-LINE scientific abstracts.

special case of the DPLVM is exactly the CRF (see Section 2.1), this case is hereinafter denoted as the CRF.


Current research in statistical machine translation (SMT) mostly settles itself in the domain of either phrase-based or syntax-based.

nevertheless, compared with the contiguous tree sequence pairs, the non-contiguous ones suffer more from the tree sequence pair redundancy problem that one non-contiguous tree sequence pair can be comprised of two or more unrelated and nonadjacent contiguous ones.


The joint HMM alignments are generated from competitive posterior 7 We used a standard training regimen: 5 iterations of model 1, 5 iterations of HMM, 3 iterations of Model 3, and 3 iterations of Model 4.  thresholding (DeNero and Klein, 2007).

second, we perform lossaugmented inference to obtainâ.


The higher the BLEU score is, or the lower the TER score is, the better the translation quality is.

the denominator is the sentence translation probability summing over all possible alignments, which can be calculated similar to IBM Model 1 in (Brown et al., 1994): Note that here only the word-based lexicon model is used to compute the confidence measure.


And we assign the same weights to each system.

we follow the work of (Sim et al., 2007;Rosti et al., 2007a;Rosti et al., 2007b;He et al., 2008) and choose the hypothesis that best agrees with other hypo-theses on average as the backbone by applying Minimum Bayes Risk (MBR) decoding (Kumar and Byrne, 2004).


the first solution estimates the transition probability as a weighted average of different distortion probabilities, whereas the second solution converts the distortion over spans to the distortion over the words in each hypothesis E(k) in the CN.


An alternative approach is to conceive the rows (component translations) in the CN as individuals, and transforms the alignment of a hypothesis against an entire network to that against the individual translations.

distortion model 1 cannot be improved by tuning L. The similar BLEU scores as shown in Table 3 implies that the incorporation of more word sequences in distortion model 1 does not lead to extra improvement.


Note that RANK requires distinct D i , so a rank k RANK rule will first apply (optimally) as soon as the kth-best inside derivation item for a given edge is removed from the queue.

2 Even for reasonable heuristics, this number can be very large; see Section 3 for empirical results.


Because of the context freedom of the grammar, any consistent heuristic for inside edge items usable in 1-best A * is also consistent for inside derivation items (and vice versa).

existing kbest extraction methods can efficiently search for top derivations, but only after an exhaustive 1-best pass.


Outside Edge Deductions (Used in KA * ) OUT-B: : : Table 3: The deduction schemata for building ouside edge items.

the 1-best Viterbi outside score for an edge is a "perfect" heuristic for any derivation of that edge.


The parsing feature weights were initialized as in ZC07, the context distance features were given small negative weights, and all other feature weights were initially set to zero.

this procedure would produce the example elaboration Expression 2 and elaborations that expand other embedded expressions, such as the quantifier in Example 1(c).


We have that X A 1 and X A 2 are 2-combinable, and their merge is the new position set X = X A 1 ∪ X A 2 = {1, 2, 3}.

in case r b < r(p), we are not guaranteed to have achieved an optimal reduction in the rank, but we can still obtain an asymptotic improvement in parsing time if we use the new productions obtained in the transformation.


Parsing time is then exponential in the input grammar size, since |G| depends on both f and r. In the development of efficient algorithms for parsing based on LCFRS the crucial goal is therefore to optimize the term f • (r + 1).

it seems unlikely that this can still be done in linear time, since the problem of binarization for LCFRS in general, i.e., without any bound on the fan-out, might not be solvable in polynomial time.


Furthermore, in a TT-MCTAG derivation the argument trees must either adjoin directly to their head tree, or they must be linked in the derivation tree to an elementary tree that attaches to the head tree, by means of a chain of adjunctions at root nodes.

items have the form [γ, p t , i, f 1 , f 2 , j] where γ ∈ i ∪ A, p is the address of a node in γ, subscript t ∈ {⊤, ⊥} specifies whether substitution or adjunction has already taken place (⊤) or not (⊥) at p, and 0 ≤ i ≤ f 1 ≤ f 2 ≤ j ≤ n are indices with i, j indicating the left and right edges of the span recognized by p and f 1 , f 2 indicating the span of a gap in case a foot node is dominated by p. We write f 1 = f 2 = − if no gap is involved.


In other words, we want to check whether a TAG derivation tree is a valid TT-MCTAG derivation tree without deciding, for every occurrence of some argument β, which of the h(β)-nodes represents its head.

here we have a derivation tree D with root node v, Lab(v) = γ q,r and a derivation tree D ′ with root node v ′ , Lab(v ′ ) = γ q ′ ,r ′ .


At a recall of 100 instances, the combination of lexical and syntactic features has the best performance for a majority of the relations, while at a recall level of 1000 instances the results are mixed.

each lexical row in Table 3 represents a single lexical feature.


Formally, we consider the following statistical learning model.

we also included another TL-NE experiment with λ T µ set to 10 2 .


Querying a concept pair using a search engine (Google), we characterize the semantic relation between the pair by leveraging the vast size of the Web.

the standard k-means algorithm is affected by the choice of seeds and the number of clusters k. as we claimed in the Introduction section, because we aim to extract relations from Wikipedia articles in an unsupervised manner, cluster number k is unknown and no good centroids can be predicted.


We define the contexts of a phrase to be small, fixed-sized windows centered on occurrences of the phrase in a large corpus.

in the literature, contexts have been defined as subject and object relations involving the word (Hindle, 1990), as the documents containing the word (Deerwester et al, 1990), or as search engine snippets for the word as a query (Sahami and Heilman, 2006).


In the two-stage cluster-based approaches such as ours, clustering is mostly decoupled from the supervised learning problem.

the training set has 203,621 tokens and the development and test set have 51,362 and 46,435 tokens, respectively.


For the most restrictive threshold (t = 0.99) though such a delay is unimportant so that it can be set to d = 0 circumventing this calibration step.

our SeSAL approach which also applies bootstrapping, aims at avoiding to deteriorate data quality by explicitly pointing human annotators to classification-critical regions.


For example, it is well known that the use of a phrase can be effective in retrieval when its constituent words appear very frequently in the collection, because each word would have a very low discriminative power for relevance.

the ranking function of the word model is equivalent to Eq.


In this paper, we propose to differentiate the value of λ in Eq.

if a constituent word occurs very rarely in the collection, it could not be effective to use the phrase even if the phrase is highly uncompositional.


We organize the rest of the paper as follows: First, we provide an overview of the previous research in this area.

for Table T 1, we have a set of queries Q, and each q ∈ Q maps to a set of url-frequency pairs.


For example, if the search engine's United States web site, which is considered as one of the most important markets in the world, was to employ such an approach, it'd only receive 74.9% accuracy by misclassifying the English queries entered from countries for which the default language is not English.

in this paper, we considered the language identification problem for search engine queries.


for example, in query pair (Toyota Camry, ), 9/13 English pages are anchored by the URLs containing keywords "toyota" and/or "camry", and 3/5 constraint documents' URLs also contain them.


Implicit relations are those inferred by the reader in the absence of a discourse connective and so are hard to identify automatically.

for example, the discourse usage in "After I went to the store, I went home" can be distinguished from the nondiscourse usage in "After May, I will go on vacation" based on the categories of their right siblings.


Some connectives are largely unambiguous, such as although and additionally, which are almost always used as discourse connectives and the relations they signal are unambiguously identified as comparison and expansion, respectively.

including pair-wise interaction features between the connective and each syntactic feature (features like connective=also-RightSibling=SBAR) raised the f-score about 1.5%, to 93.63%.


Using more than two type of knowledge together shows interesting result.

2), the com-bination of each slot of semantic frame represents the user intention symbol.


The Joint Source-Channel Model in Li et al.

table 4 shows the situation of homophones with both sound and tone taken into account.


From the research point of view, it suggests more should be considered in addition to grapheme mapping for handling Cantonese data.

although DOM has implicitly modelled the tone choice, since a specific character has a specific tone, it nevertheless heavily relies on the availability of training data.


Two types of experiments have been performed.

the proposed method only applies a part of the new word spacing states to the user input, which have probabilities that are higher than the threshold; further the method discards the other new word spacing states that have probabilities that are lower than the threshold.


After that next sentence is taken and the same procedure is repeated.

then, we build four database tables using probabilities extracted from the manually tagged corpus-word-probability table, previoustag-probability table, starting-tag-probability table  and affix-probability table.


An infinite number of partial parse trees are produced, because we cannot predict the depth of left-recursive nesting.

the adjoining operation cannot preserve lexical dependencies of partial parse trees.


there are no treebanks annotated with TSG derivations, and a treebank parse tree of n nodes is ambiguous among 2 n possible derivations.


We assume that we have the j-th morpheme and the i-th one in Figure 3.

we cannot directly compare results with ours.


The first row shows the results on only those sentences which the conversion process can convert sucessfully (as measured by converting gold-standard CCGbank derivations and comparing with PTB trees; although, to be clear, the scores are for the CCG parser on those sentences).

the second row shows the scores on those sentences for which the conversion process was somewhat lossy, but when the gold-standard CCGbank derivations are converted, the oracle F-measure is greater than 95%.


CCGbank was derived from the PTB, and so it might be considered that converting back to the PTB would be a relatively easy task, by essentially reversing the mapping Hockenmaier and Steedman (2007) used to create CCGbank.

the PtB parser we use for comparison is the publicly available Berkeley parser (Petrov and Klein, 2007).


We can retrieve positive examples from Web archive with high precision (but low recall) by manually augmenting queries with hypernyms or semantically related words (e.g., "Loft AND shop" or "Loft AND stationary").

the accuracy of the proposed approach with-EM is gradually increasing according to the percentage of added hand labeled examples.


Given a sim value (see section 2, last paragraph), we chose the best performing parameter combination manually.

(2009) to select the "best" parameters P 1 , P 2 , P 3 for the collocational graph of each MWE or head word.


each 1 for "red carpet", corpora will be collected for "red carpet" and "carpet".


Second, in the classical AP formula all relevant documents are considered relevant to the same extent.

we thus reformulate rel(f ) to give higher relevance to highly ranked features in |F V v |: where Incorporating these two modifications yields the APinc measure: Finally, we adopt the balancing approach in (Szpektor and Dagan, 2008), which, as explained in Section 2, penalizes similarity for infrequent words having fewer features (4 th property) (in our version, we truncated LIN similarity lists after top 1000 words).


These results support the design of our measure, based on the desired properties that emerged from preliminary data analysis for lexical expansion.

we aimed to quantify most effectively the above notion of feature inclusion.


Selectional preferences introduce semantic generalizations on the type of arguments preferred by the predicates.

the test set contains 4,134 pairs (covering 505 different predicates) to be classified into the appropriate role label.


The results, thus, show that selectional preferences are indeed alleviating the lexical sparseness problem.

for each head word, we will simply select the role (r) of the predicate (p) which fits best the head word (w).


We also identify clauses that are parsed as discourse segments, but are not in fact independent discourse units, and join them to the matrix clause.

recall is the total number of boundaries correct in the system's output divided by the number of total boundaries in the gold standard.


The probabilities for segment boundary insertion are learned using lexical and syntactic features.

we propose that complements of attributive and cognitive verbs (He said (that)..., I think (that)...) are not EDUs.


One trivial error caused by a lack of knowledge of the second language is using the singular noun form for weekly events: where , 1, is true iff the 1th word is 'on' and , is true iff the th word of the sentence is a noun describing day like Sunday(s).

markov logic is a probabilistic extension of finite first-order logic (Richardson and Domingos, 2006).


Thus, (Raux and Eskenazi, 2004) conducted a study on adaptation of spoken dialog systems to non-native users.

hard formulas are given maximum weight during inference.


One is the feature that indicates whether a candidate has been used as an argument of predicates in the preceding sentences ('Used' features).

baseline features are roughly those of the predicate, the noun phrase, and their relations (on the phrasal/sentential sequence and the dependency tree).


Next, focusing on the intra-sentential (Zero-Intra) and inter-sentential (Zero-Intra) zeroanaphora, the analyzer identified arguments at some level from the viewpoint of precision.

because our analyzer has scalability that can freely add new features, for our future work, we hope to adopt the case frames as new features and compare their effect.


Predicate-argument structure analysis is a type of semantic role labeling, which is an important module to extract event information such as "who did what to whom" from a sentence.

the identifier directly selects the best phrase that   where n, c, and v denote a noun phrase of an argument, the case, and the target predicate, respectively, N denotes a set of candidate arguments, d(n) is a function that returns 1 iff the phrase n becomes the argument, and M c denotes the model of the case c. In addition, f k (d(n j ) = 1, X j ) is a feature function, λ c k denotes a weight parameter of the feature function, and A denotes an article in which all sentences are parsed.


In such cases, interactive dialogue is needed to clarify the information needs and reformulate the question in a way that will help the system to find the correct answer.

to the best of our knowledge, the only available test data of multiple answer questions are list questions from tREC 2004-2007 Data.


[Sent-Head-Dep]-Is the syntactic head of a sentence dropped?

although the user study demonstrates the expected positive impact of grammatical features, an error analysis (Gupta et al., 2009) reveals some limitations to improvements that can be obtained using grammatical features that refer only to the source sentence structure, since the syntax of the source sentence is frequently not preserved in the gold standard compression.


The repetition is as small as a modifier of a noun phrase or as large as a clause.

we further confirm based on the likelihood of emitting non query-biased sentence, that there is a strong (negative) correlation among systems' likelihood score and ROUGE score, which suggests that systems are trying to improve performance based on ROUGE metrics by being biased towards the query terms.


This clearly indicates that there is a strong negative correlation between likelihood of occurrence of a non-query-term and ROUGE-2 score.

like before, Humans' and systems' summaries must now constitute low likelihood to show that they rely on querybias.


Once trained, we do feature-selection to look at what paths of rules are most predictive of certain labels.

to help eliminate redundant results, we exclude any RP that either subsumes or is subsumed by a previous (i.e.


In most systems, these approaches are treated as independent steps.

moreover, a baseline system which considers only the frequencies of words but ignores the relations between words is included for comparison.


Initially, a rough estimation of the significance of a word is given by its frequency in the document set.

algorithm 1: Hierarchical Tree Construction 1: Sort the identified key words by their frequency in the document set in descending order, denoted as T = {t 1 , t 2 ,…, t n } 2: For each t i , i from 1 to n, find the most relevant word t j from all the words before t i in T, as Here the relevance of two words is calculated by their PMI, i.e.


We assume each base ranker is most confident with its first ranked sentence and set N to 1.

both of the abovementioned "combine-then-rank" and "rank-thencombine" approaches have a common drawback.


the phrase length, the frequency that a sub-phrase appears in longer phrases.

in our experiments, we set pl = 5, ipl = 10, nv = 2, f req = 3.


For the current synchronous grammars based SMT, to some extent, the generalization ability of the grammar rules (the usability of the rules for the new sentences) can be considered as a kind of the generative power of the grammar and the disam-biguition ability to the rule candidates can be considered as an embodiment of expressive power.

the generative power refers to the ability to generate the strings of the language, and the expressive power to the ability to describe the same language with fewer or no extra ambiguities.


Figure 1 illustrates this using the words pineapple and apple pie.

instead of word sequences sequences of characters are considered, as proposed by Denoual & Lepage (2005).


And we use SRI Language Modeling Toolkit (Stolcke, 2002) to train two 5-gram language models with Kneser-Ney smoothing on the English side of the training corpus and the Xinhua portion of Gigaword corpora respectively.

lCA can be computed by using union-set (Tarjan, 1971) in lineal time.


Tree-based statistical machine translation models have made significant progress in recent years, especially when replacing 1-best trees with packed forests.

for sub translation combining, we mainly use the best-first expansion idea from cube pruning (Huang and Chiang, 2007) to combine subtranslations and generate the whole k-best translations.


However, simply applying this method might damage the accuracy of parsing.

if all the LCA are the comma's parent node, the sub-sentences are independent.


We use the t-node attribute formeme (which is not a genuine element of the semantically oriented t-layer, but rather only a technical means that facilitates modeling the transition between t-trees and surface sentence shapes) to capture syntactization of the given t-node, with values such as n:subj -semantic noun (s.n.)

there are several limitations implied by HMTMs which we have to consider before applying it to MT: (1) There can be only one labeling function on the source-language nodes, and one labeling function on the target-language nodes.


The Bengali blog data have been collected from the web blog archive (www.amarblog.com) containing 1300 sentences on 14 different topics and their corresponding user comments have been retrieved.

it signifies that the best possible reference range for valence and other emotion tags have been selected.


Ekman's six basic emotion types have been selected for reliable and semi automatic word level annotation.

given a POS tagset POS, we generate new emotion classes, 'emo_ntrl-C'|CPOS.


By applying machine learning technique to our method, we can achieve high precision while we can preserve high recall.

we defined comparative keyword in this paper as follows: A comparative keyword is formed as a word or a phrase or a long-distance-words sequence.


This was all in an effort to prevent classifiers from accessing superficial features which are reliable indicators of the document source and hence trivialise the satire detection process.

is it possible to automatically detect satire without access to the complex inferencing and real-world knowledge that humans make use of.


15 factorize over the instance index for both α-variables and β-variables.

we can classify a document x to label y : where F (•) is a map function.


For example, attributes with 'barely positive' tend to be described with a concept on a coarse level, and may belong to Must-be Quality (e,g., size in (1a)).

aspect-based sentiment analysis summarizes sentiments with diverse attributes, so that customers may have to look more closely into analyzed sentiments (Titov and McDonald, 2008).


While most research in natural language processing is dealing with identifying, extracting and classifying facts, recent years have seen a surge in research on sentiment and subjectivity (see Pang & Lee (2008) for an overview).

this suggests that the added syntactic patterns indeed manage to detect weasels that have not yet been tagged.


Those words include "user", "review" and "comment" etc.

they are related to prod-  uct but are not considered to be features.


Each node in the tree represents a feature in the product.

these methods are designed to be flexible so that they can be easily adapted to other languages.


Furthermore, we had to construct all necessary linguistic resources and define all data structures from scratch, while enjoying some advantages derived from the many existent methodologies for morpho-syntactic annotation and the high consciousness of a standardization tendency.

that means "What is the largest RAM capacity for this computer?"


in this Section, we introduce our proposed query segmentation approach, which is based on query word frequency matrix principal eigenspace similarity.


A large subset of these searches is subjective in nature, where the user is looking for different images for a single concept (Linsley, 2009).

pANDIC-TIONARY has about 80,000 senses and about 1.8 million translations at precision 0.9.


Most large software projects have bug tracking systems, e.g., Bugzilla 1 , to help global users to describe and report the bugs they encounter when using the software.

recently, there are also a number of studies that extract paraphrases from multilingual corpora (Bannard and Callison-Burch, 2005;Zhao et al., 2008).


We get in total 53,363 duplicate bug report pairs.

most paraphrases used are obtained manually.


Additionally, a dialog system can generate supportive responses like "Don't worry", "That's really sad" and "Cheer up" if it can understand the negative life events embedded in the example sentences shown in Table 1.

the training set was used for language pattern generation.


The database comprises N-grams (N=1-7) extracted from 20 billion of Japanese sentences on the web.

there are many more travel blogs in the blogos-phere.


In the proposed game, we introduce coreference to the players in a way that no linguistic knowledge is required from them.

it must be omitted for the purposes of the PlayCoref game.


The overall reduction in speed (end-to-end) is around 30%.

the CATiB POS tag set is much smaller than the PATB's.


It can avoid any pre-filtering at the document level (Resnik and Smith, 2003;Snover et al., 2008;Utiyama and Isahara, 2003;Munteanu and Marcu, 2005;Fung and Cheung, 2004).

here, both languages use a test set with a single reference.


This null alignment ratio is relatively high in comparison to the French-English alignment, in which about 9% of French sentences and 6% of English sentences are not aligned.

we use both a structured parse tree and dependency relations to extract following reordering rules.


The result proves that the proposed method can be used as a useful technique for English-Korean machine translation.

an object precedes a predicate, and all dependents precede their heads.


However, in practice, this pruning scheme does not work well with phrase tables, as many phrase pairs receive the same significance values.

in Fisher's exact test, the significance of the association of two items is measured by the probability of seeing the number of co-occurrences of the two items being the same as or higher than the one observed in the sample.


We can see that the results obtained by using the full rule table with the feature of p-values (the columns of RTABLE0 in Tables 4 and 5) are much worse than those obtained by using the pruned rule table without the feature of p-values (the columns of RTABLE1 in Tables 2 and 3).

the larger the value, the greater the significance.


In general, a Maximum Entropy (ME) framework is utilized and the feature parameters are tuned by a discriminative model.

at training time, we used a perceptron-based structure learning (PSL) algorithm to learn {w o } o∈Ω which is shown in Table 1.


A trade-off parameter λ is used to balance the contribution from the significance scores for individual words and the language model scores.

this is an oracle result since we compare the extractive summaries to the abstract for sentence selection.


there is a high concentration of chain starts and ends near the boundary which leads to a misclassification if we only combine chain starts and ends for segmentation.


We also assume that the probability of seeing a lexical chain start / end at a particular instance is independent of the starts / ends of other chains.

the story segmentation task in this work is to decide whether a hypothesized utterance boundary (provided in the tDt-2 data based on the speech recognition result) is a story boundary.


The weight vector is accumulated for all the x i morphosyntactic tag sequences in the corpus.

there has been no study to address the problem of ranking spelling suggestions.


The rationale for that change is the fact that a speech repair does not really begin until the interruption point, at which point the alteration is started and the reparandum is retroactively labelled as such.

pp/NN in Figure 5) at its root represents a reparandum with an unfinished category.


We discuss the cost of solving sub-problems by the Newton method, which iteratively updates z t by (11) Here s indicates an IS or a CD method.

the first Newton iteration of all methods discussed here takes O(l) operations.


Using data from the scientific domain, we have shown that we can achieve significant reductions in perplexity on held-out data using these models.

here, we see that Bhattacharyya and hellinger (well motivated distances for probability distributions) outperform the Euclidean metrics.


It measures the quality of a summary by counting the unit overlaps between the candidate summary and a set of reference summaries.

the knowledge on the document side, i.e.


previous work has shown that data collected through the Mechanical Turk service is reliable and comparable in quality with trusted sources (Snow et al., 2008).


On the positive side, Gamon (2004) found that the use of deep linguistic features extracted from phrase structure trees (which include syntactic dependency relations) yield significant improvements on the task of predicting satisfaction ratings in customer feedback data.

they observed that the addition of these dependency relationships does not improve performance over a feature space that includes unigrams, bigrams and trigrams.


We initialize every document a score ("1" denotes positive, and "-1" denotes negative) to represent its degree of sentiment orientation, and we call it sentiment score.

our objective is to maximize the accuracy of assigning a label in C to d i ∈D U (i = 1,…,n) utilizing the training data D L in another domain.


Mobile spam, also known as SMS spam, is a subset of spam that involves unsolicited advertising text messages sent to mobile phones through the Short Message Service (SMS) and has increasingly become a major issue from the early 2000s with the popularity of mobile phones.

any of us may send a SMS message such as "need ur advise on private loans, plz call me" or "mary, abc.com is having 70% off sale today".


• The SMS message as an end product carries the author's "fingerprints".

we also measured the ratio of special characters, the number of emoticons, and the number of special character patterns in SMS messages as features.


Critically, we also know that a missing main verb can cause major problems with comprehension.

to judge relevance to the query, we used native English speakers.


Our results show that 4%-7% of MT sentences are missing the main verb and on average, 79% of the modified sentences are judged to be more comprehensible.

once we find a VTG, its translation can be inserted into the corresponding position of the target sentence using the alignment.


Finally, the 8-bit quantized N -gram language models are represented by 9.83GB of space.

an integer within the range of 0 to 255 is coded as a 1-byte integer, the integers within the range of 256 to 65,535 are stored as 2-byte integers, and so on.


Since Y is generally between 0 and 1, the resulting discounts will be smaller than with the Chen-Goodman formula.

the discount parameters have to correct for both types of error.


Figure 2: F-measure over time for test set 98b with configurations: As can be seen, there is a small gain in performance by using seeds within the epoch of the test set, but the decay is still observable as we increase the time gap between the unlabeled data and the test set.

(2008) also observed a decaying tendency in the performance of a system for recovering capitalization over a period of six years, proposing to retrain a MaxEnt model using additional contemporary written texts.


This also seems to suggest that a Bagga and Baldwin-style vectorspace model may not be the first approach to pursue in future work on Arabic.

scoring for the ACE evaluation is done using an scoring script provided by NIsT which produces many kinds of statistics.


DTs have a natural ordering of the children of the nodes induced by the position of the corresponding words in the sentence.

presented DT kernels comparing substructures in a more sophisticated way.


For example, in the following we can propagate "Saturday" from a "Justice-Convict" event to a "Justice-Sentence" event because they both involve arguments "A state security court/state" and "newspaper/Monitor": [Sentence including EM i ] A state security court suspended a newspaper critical of the government Saturday after convicting it of publishing religiously inflammatory material.

section 4.1 below will describe the rulebased approach and section 4.2 will present the statistical learning framework respectively.


In this paper we will follow the terminology defined in the Automatic Content Extraction (ACE) 1 program: entity: an object or a set of objects in one of the semantic categories of interest: persons, locations, organizations, facilities, vehicles and weapons.

there is only one positive sample matching these conditions in the training corpora, and thus the Maximum Entropy classifier assigned a very low confidence score for propagation.


(2009) and phrase-structure annotations in the case of .

an incomplete span is denoted as I h,m where h and m are the index of the head and modifier of a dependency.


Since each derivation is defined by three fixed indices (the g-span) and one free index (the split point), the complexity of the algorithm is O(n 4 ) time and O(n 3 ) space.

recent work in dependency parsing has been restricted to applications of secondorder parsers, the most powerful of which (Carreras, 2007) requires O(n 4 ) time and O(n 3 ) space, while being limited to second-order parts.


For the baseline trained on the smaller CTB 1.0, whose training set is chapters 1-270 of CTB 5.0, the accuracy improvement is much significant, about 1.5 points over the baseline.

for the 2nd-order MST parser trained on Penn Chinese Treebank (CTB) 5.0, the classifier give an precision increment of 0.5 points.


A feature f k (i, j, r) ∈ f (i, j, r) equals 1 if it is activated by the assumption and equals 0 otherwise.

in the rest of this paper, we first describe the word-pair classification model for dependency parsing (section 2) and the generation method of projected classification instances (section 3).


Finally, the parser may assign "ate" to be the head of "with" based on the verification results.

in the figure, "projects:0-signed:1" is a target subtree in ST t .


we design bilingual subtree features, as described in Section 4, based on the constraints between the source subtrees and the target subtrees that are verified by the subtree list on the target side.


The procedure of generating the features is shown in Figure 10.

we have two target parts "W 3:0-W 1:1" and "W 3:2-W 1:0".


We first check if the added word is in the span of the corresponding words, which can be obtained through word alignment links.

as described in Section 4.3.1, the generated subtrees are verified by looking up list ST t before they are used in the parsing models.


We can approximate logical equivalence using a finite system of equations such as indicating that ∃ y and ∃ z can be permuted without changing the models of the formula.


Table 6: Results of the classification, using different feature and training sets Given the unclear dependencies of features, we chose to use a Bayesian network.

we select the resulting five best features, tuples and triples of features.


In this version of the corpus, the classification of entities is a binary one.

we will address lexical semantic features, as they tend to be effected by sparsity.


The problem of quantifying the relatedness between nodes in a graph is not a new problem, e.g., the structural equivalence and structural similarity (the SimRank in Jeh and Widom (2002) and the similarity measure in Leicht et al.

for example, if both Wikipedia and WordNet provide the semantic relation between MVP and NBA, we choose the semantic relation provided by WordNet.


And we believe that the disambiguation performance can be greatly improved by designing algorithms which can exploit these knowledge sources at best.

the recursive form of the structural semantic relatedness S ij between the node i and the node j can be written as: where λ and μ control the relative importance of the two components and N i ={j | A ij > 0} is the set of the immediate neighbors of node i; is the degree of node i.


In addition to adaptation, learned policies learn to identify when to seek information from the user to populate the user model (which is initially set to not sure).

novice users knew just one jargon expression, Int2 knew seven, and Expert users knew all thirteen jargon expressions.


Since the agent starts the conversation with no knowledge about the user, it may try to use more exploratory moves to learn about the user, although they may be inappropriate.

in all the above systems, the user's knowledge is assumed to be accurately represented in an initial user model using which the system adapts its language.


When the user carries out an instruction, the system state is updated and the next instruction is given.

in reality, designers might want their system to account for user's changing knowledge as well.